In the Fibroid Growth Study(FGS) we collected a variety of data, such as MRI measurements of fibroid volumes over time (up to 4 time points), gene expression microarray data obtained on tumors from women who opted for surgery, etc. We are presently analyzing data obtained from that study. This research was motivated by our recent publications (Peddada et al., PNAS, 2008 and Baird et al.,Fertility and Sterility, 2010) regarding growth of fibroids in pre-menopausal women. The study of uterine leiomyomata (fibroids) provides a unique opportunity to investigate the physiological and molecular determinants of hormone dependent tumor growth and spontaneous tumor regression. We conducted a longitudinal clinical study of premenopausal women with fibroids that showed significantly different growth rates between white and black women depending on their age. We now report the gene expression data from tumors collected in this study. Total RNA from 52 fibroid and 8 myometrial samples were analyzed using Affymetrix Gene Chip expression arrays. Gene expression data was first compared between all fibroids and normal myometrium and then between fibroids defined as rapidly growing and fibroids defined as non-growing based on age and race of the women from which these tumors were collected. Genes that were found significant in pairwise comparisons were further analyzed for canonical pathways, networks and biological functions using the Ingenuity Pathway Analysis (IPA) software. Whereas our comparison of leiomyoma to myometrium produced a very large list of genes highly similar to numerous previous studies, distinct sets of genes and signaling pathways were identified in comparisons of growing and non-growing fibroids. Key among these were genes associated with regulation of apoptosis. To our knowledge, this is the first study to compare growing and non-growing tumors from a clinical study in order to differentiate the molecular signals specific for tumor growth from the complexity of molecular signals that give rise to pleomorphic tumor phenotypes.